How to Build End-to-End Deep Learning Models for Self-Driving Cars

Introduction

Modern autonomous‑driving technology owes much to the DARPA Grand Challenges, which began in 2004 to encourage unmanned ground vehicles. The first two contests demonstrated rapid progress, with Stanford’s team (led by Sebastian Thrun) winning in 2005 and the 2007 urban‑road challenge spawning Google’s self‑driving project. Two widely used autonomy‑level standards are defined by the U.S. National Highway Traffic Safety Administration (NHSTA) and SAE International.

Question 1: Designing an End‑to‑End Autonomous Driving Model

The goal is to create a model that maps raw sensor inputs directly to vehicle control signals, eliminating hand‑crafted rules and sub‑task pipelines (lane detection, scene abstraction, path planning, etc.). An end‑to‑end approach simplifies the system, improves efficiency, and allows the network to learn implicit sub‑tasks.

A representative work is NVIDIA’s 2016 PilotNet model. PilotNet is a nine‑layer convolutional‑fully‑connected network that takes raw images from three forward‑facing cameras (converted to YUV) and predicts the steering angle.

Simulation and Real‑World Results

In simulation, a manual intervention is triggered when the vehicle deviates more than one metre from the lane centre, with each intervention costing roughly six seconds. PilotNet achieved a 90% success rate in simulation and 98% in on‑road testing, indicating strong real‑world performance.

Question 2: Reinforcement‑Learning Based Decision System

Traditional rule‑based decision modules struggle with edge cases and multi‑agent interactions. A reinforcement‑learning (RL) framework can let the vehicle learn safe policies from data. The Mobileye multi‑agent RL system is a notable example, addressing unpredictable behaviours of surrounding agents and emphasizing safety in unforeseen scenarios.

Extension and Summary

Autonomous driving systems are highly complex; this article covered only a subset of research topics. Deep learning is widely applied to perception tasks such as image classification and segmentation, while its use in control and decision‑making remains exploratory. Readers are encouraged to consult the cited references for deeper study.

References

ADMINISTRATION N H T S. Preliminary statement of policy concerning automated vehicles[C]//2013.

SMITH B W. SAE levels of driving automation[C]//2013.

BOJARSKI M, TESTA D D, DWORAKOWSKI D, et al. End to end learning for self‑driving cars. https://arxiv.org/abs/1604.07316. 2016.

BOJARSKI M, YERES P, CHOROMANSKA A, et al. Explaining how a deep neural network trained with end‑to‑end learning steers a car. https://arxiv.org/abs/1704.07911. 2017.

SHALEV‑SHWARTZ S, SHAMMAH S, SHASHUA A. Safe, multi‑agent, reinforcement learning for autonomous driving. https://arxiv.org/abs/1610.03295. 2016.